OSCR

The In Vivo Microstructural Profile of Human Hippocampal Subfield CA1 and Its Relation to Memory Performance.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches
  1. [1] § Methods › Vessel Segmentation and Vessel Distance Mapping (VDM) ↔ omelette.py, lines 96–223 · score 0.76 · vessel enhancement filters, top hat transformation, Mattern, OMELETTE, vasculature, Jerman
  2. [2] § Methods › Vessel Segmentation and Vessel Distance Mapping (VDM) ↔ data_and_results/DRIVE/drive_notebook.ipynb, lines 57–184 · score 0.74 · vessel enhancement filters, top hat transformation, Mattern, vasculature, Jerman, voxel
  3. [3] § Methods › CA1 Depth Segmentation (Layer Analyses Pipeline) ↔ Example_Analysis.py, lines 11–78 · score 0.64 · bounding box, CA1 layers, qT1 images, cropped, upsampled, masks
  4. [4] § Methods › CA1 Depth Segmentation (Layer Analyses Pipeline) ↔ Example_Analysis.py, lines 11–78 · score 0.62 · bounding box, CA1 layer, qT1 image, equidistant, cropped, upsampled
  5. [5] § Methods › CA1 Depth Segmentation (Layer Analyses Pipeline) ↔ Crop_and_Upsample_MRI.py, lines 51–85 · score 0.60 · bounding box, qT1 image, CA1 layer, cropped, upsampled, maps
  6. [6] § Methods › CA1 Depth Segmentation (Layer Analyses Pipeline) ↔ Crop_and_Upsample_MRI.py, lines 1–16 · score 0.56 · CA1 masks, nibabel, resample, cropped, upsampled, isotropic

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 158 lines · 5.7 KB · no license · 2 matches

  1. import os
  2. import numpy as np
  3. import pandas as pd
  4. import nibabel as nib
  5. import matplotlib.pyplot as plt
  6. from matplotlib.ticker import MaxNLocator
  8. # --- Paths ---
  9. data_path = "/path/to/subject_folders"
  11. # --- Subjects ---
  12. subjects = os.listdir(data_path)
  14. sides = ["LH", "RH"]
  15. ROI = "CA1"
  16. all_data = []
  17. need_csv = 0 # Set this to 1, if you need to get layer data in a csv file
  18. do_plot = 1
  20. if need_csv:
  21. # --- Processing Loop ---
  22. for sub in subjects:
  23. for side in sides:
  24. # File paths
  25. subj_layers_path = os.path.join(data_path, sub, "upsampled_layer_files", f"{sub}_{side}_CA1_layers_equidist.nii.gz")
  26. subj_qt1_path = os.path.join(data_path, sub, "upsampled_data", f"{sub}_qt1_{side}_cropped_upsampled.nii.gz")
  28. if not os.path.exists(subj_qt1_path) or not os.path.exists(subj_layers_path):
  29. print(f"Skipping {sub} | {side} due to missing files.")
  30. continue
  32. # Load images
  33. qt1_img = nib.load(subj_qt1_path)
  34. layer_img = nib.load(subj_layers_path)
  36. # --- Load data arrays ---
  37. qt1_data = qt1_img.get_fdata()
  38. layer_data = layer_img.get_fdata()
  40. # Bounding box for non-zero mask
  41. nonzero_coords = np.array(np.nonzero(layer_data))
  42. min_coords = nonzero_coords.min(axis=1)
  43. max_coords = nonzero_coords.max(axis=1) + 1
  45. layer_crop = layer_data[min_coords[0]:max_coords[0],
  46. min_coords[1]:max_coords[1],
  47. min_coords[2]:max_coords[2]]
  48. qt1_crop = qt1_data[min_coords[0]:max_coords[0],
  49. min_coords[1]:max_coords[1],
  50. min_coords[2]:max_coords[2]]
  52. print(f"[{sub} | {side}] Cropped shapes - Layer mask: {layer_crop.shape}, T1 map: {qt1_crop.shape}")
  54. # Extract values
  55. layer_ids = np.unique(layer_crop)
  56. layer_ids = layer_ids[layer_ids != 0]
  58. for layer_id in layer_ids:
  59. mask = layer_crop == layer_id
  60. qt1_values = qt1_crop[mask]
  62. if qt1_values.size > 0:
  63. mean_qt1 = np.mean(qt1_values)
  64. all_data.append({
  65. "Subject": sub,
  66. "Hemisphere": side.capitalize(),
  67. "Layer": int(layer_id),
  68. "Myelin": mean_qt1
  69. })
  73. # --- Create DataFrame ---
  74. df = pd.DataFrame(all_data)
  75. df["Subject"] = df["Subject"].astype(str)
  77. # --- Save CSV ---
  78. df.to_csv("path/to/layer_data.csv", index=False)
  81. # --- Load the CSV with Layer Myelin Data ---
  82. path_final_data = ("path/to/layer_data.csv")
  83. df = pd.read_csv(path_final_data)
  85. if do_plot:
  86. # --- Flip layer numbering --- Only necessary if you accidentaly assigne labels 1 and 2 the the opposite surfaces during rim generation...if you did it correctly, ignore this.
  87. print(df["Layer"])
  88. df["Layer_flipped"] = 22 - df["Layer"]
  89. # --- Assign Layer Zones ---
  90. def assign_zone(layer):
  91. if 1 <= layer <= 7:
  92. return "Inner"
  93. elif 8 <= layer <= 14:
  94. return "Middle"
  95. elif 15 <= layer <= 21:
  96. return "Outer"
  97. return None
  99. df["LayerZone"] = df["Layer_flipped"].apply(assign_zone)
  100. df = df.dropna(subset=["LayerZone", "Myelin"])
  102. # --- Summary Table ---
  103. def get_summary_table(df):
  104. summary = df.groupby("LayerZone")["Myelin"].agg(["mean", "std", "count"]).reset_index()
  105. summary = summary.rename(columns={"mean": "Mean_Myelin", "std": "STD_Myelin", "count": "N"})
  106. summary["LayerZone"] = pd.Categorical(summary["LayerZone"], categories=["Inner", "Middle", "Outer"], ordered=True)
  107. summary = summary.sort_values("LayerZone")
  108. summary = summary.rename(columns = {"LayerZone" : "Compartment"})
  109. print("\nSummary:")
  110. print(summary)
  112. get_summary_table(df)
  114. # --- Plot qT1 Profiles (Layer on x-axis) ---
  115. fig, axes = plt.subplots(1, 2, figsize=(10, 6), sharey=True, sharex=True)
  117. # Colors for zones (Inner=red, Middle=yellow, Outer=blue)
  118. layer_zones = {"Inner": (1, 7.5), "Middle": (7.5, 14.5), "Outer": (14.5, 21)}
  119. colors = {"Inner": "#ffcccc", "Middle": "#ffff99", "Outer": "#add8e6"}
  121. for ax, hemi in zip(axes, ["Rh", "Lh"]):
  122. hemi_df = df[df["Hemisphere"] == hemi]
  124. # Plot individual subjects
  125. for subject in hemi_df["Subject"].unique():
  126. subj_df = hemi_df[hemi_df["Subject"] == subject]
  127. ax.plot(subj_df["Layer_flipped"], subj_df["Myelin"], color='black', alpha=0.5)
  129. # Plot colored zones
  130. for zone, (start, end) in layer_zones.items():
  131. ax.axvspan(start, end, color=colors[zone], alpha=0.5, zorder=0)
  133. # Plot group mean
  134. group_mean = hemi_df.groupby("Layer_flipped")["Myelin"].mean()
  135. ax.plot(group_mean.index, group_mean.values, color='red', lw=2, label='Group Mean')
  136. ax.tick_params(axis='x', labelsize=18) # Change x tick label font size
  137. ax.tick_params(axis='y', labelsize=18) # Change y tick label font size
  139. ax.set_title(f"qT1 Profile - {hemi} CA1", fontweight='bold', size=24)
  140. ax.set_xlabel("Layer", size = 18)
  141. ax.set_ylabel("qT1 (ms)", size= 18)
  143. ax.set_xlim(2, 21)
  144. ax.set_ylim(1200, 2800)
  145. #ax.grid(True)
  147. # Show only whole numbers on x-axis
  148. ax.xaxis.set_major_locator(MaxNLocator(integer=True))
  150. plt.yticks(size=18)
  151. plt.xticks(size=18)
  152. plt.tight_layout()
  153. plt.savefig("path/to/where_you_want_to_save_it", bbox_inches = 'tight')
  154. plt.show()
  157. plt.tight_layout()
  158. plt.show()

Example_Analysis.py at commit 2feccc7, no license · at the source

Overview

Authors: Dayana Hayek1, Joseph Höpker Fernandes2,3, Niklas Vockert3, Berta Garcia‐Garcia1,3, Hendrik Mattern3,4,5, Niklas Behrenbruch2, Larissa Fischer3,5,6, Avinash Kalyani1,7, Juliane Doehler1,3, Dorothea Hämmerer1,5,8, Yeo‐Jin Yi1,3, Stefanie Schreiber3,5,9, Anne Maass2,3,5, Esther Kuehn5,7,10
ORCID iDs: Dayana Hayek, Joseph Höpker Fernandes, Niklas Vockert, Hendrik Mattern, Niklas Behrenbruch, Larissa Fischer, Avinash Kalyani, Juliane Doehler, Dorothea Hämmerer, Yeo‐Jin Yi, Stefanie Schreiber, Anne Maass, Esther Kuehn
  1. Institute of Cognitive Neurology and Dementia Research (IKND) Otto‐von‐Guericke University Magdeburg Germany
  2. Faculty of Natural Sciences Otto von Guericke University Magdeburg Magdeburg Germany
  3. German Center for Neurodegenerative Diseases (DZNE) Magdeburg Germany
  4. Department Biomedical Magnetic Resonance (BMMR) Otto‐von‐Guericke‐Universität Magdeburg Germany
  5. Center for Behavioral Brain Sciences (CBBS) Magdeburg Germany
  6. Department of Neurobiology and Behavior University of California Irvine USA
  7. Hertie Institute for Clinical Brain Research (HIH) Tübingen Germany
  8. Department of Psychology University of Innsbruck Innsbruck Austria
  9. Department of Neurology Otto‐von‐Guericke University Magdeburg Magdeburg Germany
  10. German Center for Neurodegenerative Diseases (DZNE) Tübingen Germany
Institutions: Otto-von-Guericke-Universität Magdeburg (Germany); German Center for Neurodegenerative Diseases (Germany); Center for Behavioral Brain Sciences (Germany); University of California, Irvine (United States); Hertie Institute for Clinical Brain Research (Germany); Universität Innsbruck (Austria)
Journal: Human brain mapping, volume 47, issue 7, article e70542
Dates: received 11 November 2025; accepted 25 April 2026; published online 13 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/hbm.70542 · PMID 42125937 · PMCID PMC13169157 · OpenAlex W7161012161
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism)
Methods: Connectivity, Statistics
Keywords: hippocampus, layers, microstructure, myelination, ultra‐high resolution, vascularization
MeSH: CA1 Region, Hippocampal*, Memory*, Nerve Fibers, Myelinated*, Adult, Age Factors, Female, Humans, Magnetic Resonance Imaging, Male, Sex Factors, Young Adult (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (949609)
Citations: not cited yet (Europe PMC); 89 references in the paper

Abstract

The hippocampal CA1 subregion supports learning, memory formation, and spatial navigation. Although its three‐layered architecture has been described in ex vivo investigations, the in vivo microstructural profile of CA1 and its relation to individual variations in memory performance remain poorly characterized. In this study, we used ultra‐high field structural MRI at 7 Tesla to investigate the depth‐dependent myelination patterns (measured by quantitative T1) of CA1 in younger adults, their relation to the local arterial architecture, and their association with individual differences in cognitive functions, specifically memory performance. Results show that left and right CA1 present depth‐dependent patterns of myelination, with the outer and inner compartments showing higher myelination than the middle compartment. No significant relationship between layer‐specific myelination of CA1 and distance to the nearest artery was observed. Right CA1 was found to be more myelinated than left CA1. Pairwise correlations and regression models showed that higher left CA1 myelination is linked to higher accuracy in object localization. Together, our data demonstrate the feasibility of describing the three‐layered myelin architecture of CA1 in vivo, and provide information on how alterations in the architecture of CA1 may relate to alterations in cognitive performance in younger adults.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

hmattern/omelette

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 189bde5902805a5cfd4b7c5eeada27b075156bbe, 27 May 2021
Languages: Python (9), Jupyter (1)
Size: 135 files, 10 scripts
Software Heritage: not archived
Found in: the text, “Vessel Segmentation and Vessel Distance Mapping ”
Holds: README, license file, environment (requirements.txt), 1 notebook
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (10 files), scikit-image (7 files), Matplotlib (6 files), Pillow (3 files), h5py (1 file), NiBabel (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
12 files
At the source: gitlab.com/hmattern/omelette

zeconyser/the-in-vivo-microstructural-profile-of-human-hippocampal-subfield-ca1

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2feccc75be8919f6d98bef85fe19bede2eae69d3, 27 April 2026
Languages: Python (4), Shell (1)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: the end of the paper
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NiBabel (4 files), NumPy (4 files), Matplotlib (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/Zeconyser/The-In-vivo-Microstructural-Profile-of-Human-Hippocampal-Subfield-CA1/tree/main

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 15 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 6 keywords, 11 MeSH terms, 1 funder, 74 references.

Cite

This paper

Hayek, D., Fernandes, J. H., Vockert, N., Garcia‐Garcia, B., Mattern, H., Behrenbruch, N., Fischer, L., Kalyani, A., Doehler, J., Hämmerer, D., Yi, Y., Schreiber, S., Maass, A., & Kuehn, E. (2026). The In Vivo Microstructural Profile of Human Hippocampal Subfield CA1 and Its Relation to Memory Performance. Human brain mapping, 47(7), e70542. https://doi.org/10.1002/hbm.70542

BibTeX

@article{hayek2026vivo,
author = {Hayek, Dayana and Fernandes, Joseph Höpker and Vockert, Niklas and Garcia‐Garcia, Berta and Mattern, Hendrik and Behrenbruch, Niklas and Fischer, Larissa and Kalyani, Avinash and Doehler, Juliane and Hämmerer, Dorothea and Yi, Yeo‐Jin and Schreiber, Stefanie and Maass, Anne and Kuehn, Esther},
title = {{The In Vivo Microstructural Profile of Human Hippocampal Subfield CA1 and Its Relation to Memory Performance}},
journal = {Human brain mapping},
year = {2026},
month = may,
volume = {47},
number = {7},
pages = {e70542},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70542},
url = {https://doi.org/10.1002/hbm.70542},
pmid = {42125937},
pmcid = {PMC13169157}
}

RIS

TY - JOUR
AU - Hayek, Dayana
AU - Fernandes, Joseph Höpker
AU - Vockert, Niklas
AU - Garcia‐Garcia, Berta
AU - Mattern, Hendrik
AU - Behrenbruch, Niklas
AU - Fischer, Larissa
AU - Kalyani, Avinash
AU - Doehler, Juliane
AU - Hämmerer, Dorothea
AU - Yi, Yeo‐Jin
AU - Schreiber, Stefanie
AU - Maass, Anne
AU - Kuehn, Esther
TI - The In Vivo Microstructural Profile of Human Hippocampal Subfield CA1 and Its Relation to Memory Performance
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/05/01
VL - 47
IS - 7
SP - e70542
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70542
UR - https://doi.org/10.1002/hbm.70542
LA - en
ER -

CSL-JSON

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